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Generative AI Leader Practice Question: Evaluating the ROI of deploying a GenAI-powered…
A company is evaluating the ROI of deploying a GenAI-powered code review assistant. Which metric would BEST capture the quality improvement from using the assistant?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Defect escape rate (bugs found in production)
Defect escape rate measures bugs that reach production; a reduction indicates higher code quality. Productivity metrics like lines of code per hour measure speed, not quality. User acceptance is a satisfaction measure, not direct quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Number of code reviews completed per week
Why it's wrong here
Review throughput counts volume, not quality, so it cannot evidence defect detection or review depth. It is tempting because throughput is easy to instrument and rises when the assistant accelerates reviews, but that measures speed and capacity rather than the quality improvement the ROI question targets.
- ✗
Lines of code written per developer per day
Why it's wrong here
Lines of code measures developer output volume, which often increases with lower-quality code, so it cannot capture review quality. It is tempting as a classic productivity metric, but it would suit measuring coding throughput or velocity, not the defect-detection and review-quality gains the assistant delivers.
- ✗
Developer satisfaction survey score
Why it's wrong here
Satisfaction scores capture subjective developer sentiment, not objective code review quality such as defect detection or false-positive rates. It is tempting because surveys are cheap and adoption-related, but they would be the right choice when measuring user experience or tool acceptance rather than quality improvement.
- ✓
Defect escape rate (bugs found in production)
Why this is correct
Defect escape rate measures bugs reaching production, directly reflecting review quality rather than volume or speed. A code review assistant that catches genuine defects lowers this rate, making it the metric that best captures quality improvement, unlike throughput or acceptance-rate measures.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.